------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\swhitt\Desktop\Gender and Accountability Replication Files\SCT gender and accountabili
> ty replication data long format log file.log
  log type:  text
 opened on:  30 Nov 2023, 15:59:00

. do "C:\Users\swhitt\Desktop\Gender and Accountability Replication Files\SCT gender and accountability repl
> ication data long format do file.do"

. *Gender, Agency, and Accountability for ISIS Violence: Public Perspectives from Mosul, Iraq
. *Replication Instructions
. 
. *Vera Mironova and Sam Whitt
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA usi
> ng the long format of the 
. *dataset “SCT gender and accountability replication data long format.dta”. 
. 
. *Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. 
. 
. *Note: You may need to install STATA packages for cibar, catcibar  and iebaltab commands. 
. *Use findit with command name to identify and download the appropriate packets to install. To install comm
> ands enter the following:
. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph pla
> y command. 
. *To format a graph, simply run the command to generate the graph in the do file in STATA, then open the “G
> raph Editor” in STATA and click on 
. *the GREEN “Play Recording” button, then select “Browse” to select the grec file from among Replication fi
> les. 
. *The name of the grec file is indicated in the note below the graph command in the do file for the specifi
> c graph you wish to format. 
. *This should automatically format the graph, which you may then save to a location of your choosing.
. 
. *Manuscript Text
. 
. *Note: replication of data discussed in the text as well as key variables is available in the wide format 
> dataset and do file. 
. *Many variables in the wide format dataset are also included in the long format version, as well as some u
> nique variables necessary
. *for analysis of stacked data. 
. 
. *Manuscript Tables and Figures
. 
. *Figure 1. ISIS punishment preferences (no gender prime)
. *long format
. 
. cibar decision, over2(category) over1(idpcamp)

. 
. *Note additional formatting requires the "Figure 1 long formatting.grec" file with the command graph play 
> "Figure 1 long formatting.grec"
. 
. *Figure 2. ISIS punishment preferences (gender primes)
. *long format
. 
. cibar expgroup, over1(treatment) over2(mosul)

. 
. *Note additional formatting requires the "Figure 2 long formatting.grec" file with the command graph play 
> "Figure 2 long formatting.grec"
. 
. *Figure 3. Women's agency and accountability
. *see wide format
. 
. *Table 2. Women’s Agency for Violence and Accountability (Ordered Probit Regression)
. *long format
. 
. oprobit punmalefemale femaleworkertxt, cluster(id)

Iteration 0:   log pseudolikelihood = -1054.7262  
Iteration 1:   log pseudolikelihood = -1048.2441  
Iteration 2:   log pseudolikelihood = -1048.2441  

Ordered probit regression                       Number of obs     =        689
                                                Wald chi2(1)      =      31.46
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1048.2441               Pseudo R2         =     0.0061

                                      (Std. Err. adjusted for 345 clusters in id)
---------------------------------------------------------------------------------
                |               Robust
  punmalefemale |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
femaleworkertxt |   .2927016    .052181     5.61   0.000     .1904288    .3949744
----------------+----------------------------------------------------------------
          /cut1 |  -.5382295   .0645024                      -.664652   -.4118071
          /cut2 |   .3216008   .0604422                      .2031362    .4400653
          /cut3 |   .8625207   .0666747                      .7318407    .9932007
          /cut4 |   1.231688   .0756904                      1.083337    1.380038
---------------------------------------------------------------------------------

. oprobit punmalefemale femaleworkertxt##c.agencyaffirm isisvictim female age education income professional 
> laborer unemployed moved, cluster(id)

Iteration 0:   log pseudolikelihood =  -989.1661  
Iteration 1:   log pseudolikelihood = -868.74891  
Iteration 2:   log pseudolikelihood =   -868.182  
Iteration 3:   log pseudolikelihood = -868.18174  
Iteration 4:   log pseudolikelihood = -868.18174  

Ordered probit regression                       Number of obs     =        639
                                                Wald chi2(12)     =     191.26
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -868.18174               Pseudo R2         =     0.1223

                                                     (Std. Err. adjusted for 320 clusters in id)
------------------------------------------------------------------------------------------------
                               |               Robust
                 punmalefemale |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
               femaleworkertxt |
   female worker (saraworker)  |  -.0317248   .1760908    -0.18   0.857    -.3768565    .3134069
                  agencyaffirm |   1.038042   .2951877     3.52   0.000     .4594846    1.616599
                               |
femaleworkertxt#c.agencyaffirm |
   female worker (saraworker)  |   .7444369   .2838082     2.62   0.009      .188183    1.300691
                               |
                    isisvictim |  -.0216205   .0918314    -0.24   0.814    -.2016067    .1583657
                        female |  -.2931273   .1374401    -2.13   0.033    -.5625049   -.0237497
                           age |   .0007234   .0057249     0.13   0.899    -.0104972    .0119439
                     education |   .1639311   .0770439     2.13   0.033     .0129278    .3149344
                        income |   .3070845   .0665368     4.62   0.000     .1766748    .4374941
                  professional |  -.1423502    .190088    -0.75   0.454    -.5149158    .2302153
                       laborer |  -.2794866   .1793049    -1.56   0.119    -.6309177    .0719446
                    unemployed |  -.1752524   .1858581    -0.94   0.346    -.5395276    .1890228
                         moved |    .166977   .1214925     1.37   0.169     -.071144    .4050979
-------------------------------+----------------------------------------------------------------
                         /cut1 |    .719088   .3243427                      .0833881    1.354788
                         /cut2 |   1.783391   .3292339                      1.138104    2.428677
                         /cut3 |   2.458531   .3270506                      1.817523    3.099538
                         /cut4 |   2.908437   .3325495                      2.256652    3.560222
------------------------------------------------------------------------------------------------

. oprobit punmalefemale femaleworkertxt##c.judicialdenial isisvictim female age education income professiona
> l laborer unemployed moved, cluster(id)

Iteration 0:   log pseudolikelihood = -1051.9259  
Iteration 1:   log pseudolikelihood = -933.14173  
Iteration 2:   log pseudolikelihood = -932.65121  
Iteration 3:   log pseudolikelihood = -932.65096  
Iteration 4:   log pseudolikelihood = -932.65096  

Ordered probit regression                       Number of obs     =        687
                                                Wald chi2(12)     =     233.00
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -932.65096               Pseudo R2         =     0.1134

                                                       (Std. Err. adjusted for 344 clusters in id)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                   punmalefemale |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                 femaleworkertxt |
     female worker (saraworker)  |   .0622088   .1236506     0.50   0.615     -.180142    .3045596
                  judicialdenial |   .2701929   .2240698     1.21   0.228    -.1689757    .7093616
                                 |
femaleworkertxt#c.judicialdenial |
     female worker (saraworker)  |   .5850785   .2176894     2.69   0.007      .158415    1.011742
                                 |
                      isisvictim |   .0824024   .0878478     0.94   0.348    -.0897762    .2545809
                          female |    -.35239   .1352943    -2.60   0.009    -.6175618   -.0872181
                             age |   .0012691   .0052399     0.24   0.809    -.0090009    .0115391
                       education |   .1632937   .0727546     2.24   0.025     .0206974      .30589
                          income |   .4730969   .0647127     7.31   0.000     .3462623    .5999315
                    professional |   -.195363    .184569    -1.06   0.290    -.5571115    .1663855
                         laborer |  -.3589112   .1702984    -2.11   0.035    -.6926901   -.0251324
                      unemployed |  -.1480034   .1816458    -0.81   0.415    -.5040226    .2080158
                           moved |     .23706    .116601     2.03   0.042     .0085263    .4655937
---------------------------------+----------------------------------------------------------------
                           /cut1 |   .6048175   .3253461                     -.0328492    1.242484
                           /cut2 |   1.659524   .3291168                      1.014467    2.304581
                           /cut3 |   2.311371   .3324871                      1.659708    2.963034
                           /cut4 |   2.743829   .3406103                      2.076245    3.411413
--------------------------------------------------------------------------------------------------

. oprobit punmalefemale mosul##femaleworkertxt isisvictim female age education income professional laborer u
> nemployed moved, cluster(id)

Iteration 0:   log pseudolikelihood = -1054.7262  
Iteration 1:   log pseudolikelihood = -899.57571  
Iteration 2:   log pseudolikelihood = -898.45394  
Iteration 3:   log pseudolikelihood = -898.45238  
Iteration 4:   log pseudolikelihood = -898.45238  

Ordered probit regression                       Number of obs     =        689
                                                Wald chi2(12)     =     284.60
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -898.45238               Pseudo R2         =     0.1482

                                                    (Std. Err. adjusted for 345 clusters in id)
-----------------------------------------------------------------------------------------------
                              |               Robust
                punmalefemale |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                      1.mosul |   .8051188   .1477793     5.45   0.000     .5154768    1.094761
                              |
              femaleworkertxt |
  female worker (saraworker)  |   .0491823   .0989681     0.50   0.619    -.1447915    .2431562
                              |
        mosul#femaleworkertxt |
1#female worker (saraworker)  |   .5169472   .1317882     3.92   0.000      .258647    .7752473
                              |
                   isisvictim |   .0747402   .0874688     0.85   0.393    -.0966955    .2461759
                       female |  -.2477708    .126592    -1.96   0.050    -.4958865    .0003449
                          age |  -.0021506   .0047863    -0.45   0.653    -.0115315    .0072303
                    education |   .0804322   .0772048     1.04   0.298    -.0708863    .2317508
                       income |   .2096228   .0646542     3.24   0.001      .082903    .3363426
                 professional |  -.0797201   .1822959    -0.44   0.662    -.4370135    .2775733
                      laborer |  -.2298158    .170385    -1.35   0.177    -.5637643    .1041327
                   unemployed |  -.1130177   .1653816    -0.68   0.494    -.4371596    .2111242
                        moved |   .1500447   .1166707     1.29   0.198    -.0786256     .378715
------------------------------+----------------------------------------------------------------
                        /cut1 |   .1447695   .3117771                     -.4663024    .7558414
                        /cut2 |   1.259427   .3173392                      .6374533      1.8814
                        /cut3 |   1.949934    .315895                      1.330791    2.569077
                        /cut4 |   2.425771   .3203942                      1.797809    3.053732
-----------------------------------------------------------------------------------------------

. 
. *Appendix Replication
. 
. *Mosul Sampling Locations
. *see wide format
. 
. *Summary of Variables in Manuscript Analysis
. *see wide format
. 
. *Figure 1 Box-Whisker Plots of Punishing ISIS from Manuscript Figure 1
. *see also wide format
. *(Alternative) Long-format
. 
. cibar decision, over1(category) over2(mosul)

. 
. *Figure 2 Box-Whisker Plots of Vignette Punishments from Manuscript Figure 2.
. *Long format
. 
. graph box nasimloyal-linahigh

. 
. *Note additional formatting requires the "Figure 2a long formatting.grec" file with the command graph play
>  "Figure 2a long formatting.grec"
. 
. *Figure 3. Perceptions of Judicial Affirmation/Denial of Women’s Agency
. *Long format
. 
. cibar denialgroup, over1(denialcat) over2(mosul)

. 
. *Note additional formatting requires the "SA Figure 3 long formatting.grec" file with the command graph pl
> ay "SA Figure 3 long formatting.grec"
. 
. *Table 1. Factor Analysis of Judicial Affirmation/Denial Index
. *see wide format
. 
. *Figure 4. Perceptions of Women’s Agency in ISIS
. *Long format
. 
. cibar affirmgroup, over1(affirmcat) over2(mosul)

. 
. *Note additional formatting requires the “SA Figure 4 long formatting.grec” file with the command graph pl
> ay “SA Figure 4 long formatting.grec”
. 
. *Table 2. Factor Analysis of Agency Affirmation Index
. *see wide format
. 
. *Figure 5. Perceptions of ISIS Victimization by Location
. *see wide format
. 
. *Table 3. Factor Analysis of Victimization Index
. *see wide format
. 
. *Further Analysis of Gender and ISIS Punishment Preferences
. *Long format
. 
. factor dleaders dfighters dff dreligpolice dcivcomsup dcivadmin dcivlabor dcivpolice dtaxpayers
(obs=579)

Factor analysis/correlation                      Number of obs    =        579
    Method: principal factors                    Retained factors =          4
    Rotation: (unrotated)                        Number of params =         30

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      5.09306      3.75878            0.8107       0.8107
        Factor2  |      1.33428      1.20667            0.2124       1.0231
        Factor3  |      0.12760      0.12395            0.0203       1.0435
        Factor4  |      0.00366      0.02783            0.0006       1.0440
        Factor5  |     -0.02417      0.01701           -0.0038       1.0402
        Factor6  |     -0.04118      0.01369           -0.0066       1.0336
        Factor7  |     -0.05487      0.00894           -0.0087       1.0249
        Factor8  |     -0.06382      0.02881           -0.0102       1.0147
        Factor9  |     -0.09262            .           -0.0147       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(36) = 5027.77 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3   Factor4 |   Uniqueness 
    -------------+----------------------------------------+--------------
        dleaders |   0.5674    0.5000    0.1925    0.0066 |      0.3910  
       dfighters |   0.8318    0.4245   -0.0656   -0.0045 |      0.1236  
             dff |   0.8067    0.4723    0.0055    0.0025 |      0.1262  
    dreligpolice |   0.8144    0.3001   -0.1302    0.0006 |      0.2297  
      dcivcomsup |   0.8235   -0.3466   -0.1741    0.0142 |      0.1712  
       dcivadmin |   0.8707   -0.3869   -0.0057    0.0054 |      0.0921  
       dcivlabor |   0.8357   -0.4469    0.0668   -0.0157 |      0.0971  
      dcivpolice |   0.7913   -0.3263    0.1701   -0.0119 |      0.2384  
      dtaxpayers |   0.0669   -0.1221    0.0745    0.0545 |      0.9721  
    ---------------------------------------------------------------------

. *alpha dleaders dfighters dff dreligpolice dcivcomsup dcivadmin dcivlabor dcivpolice, gen(alphapunishisis)
. 
. *Table 4. Punishment of ISIS (OLS Regression)
. *Long format
. 
. reg alphapunishisis mosul isisvictim female age education income professional laborer unemployed moved, ro
> bust

Linear regression                               Number of obs     =        595
                                                F(10, 584)        =     151.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6642
                                                Root MSE          =     .60845

------------------------------------------------------------------------------
             |               Robust
alphapunis~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       mosul |   1.646449    .069057    23.84   0.000     1.510819    1.782079
  isisvictim |   .0380756   .0329114     1.16   0.248    -.0265636    .1027148
      female |  -.0105107   .0629885    -0.17   0.868    -.1342222    .1132008
         age |   .0005389   .0023619     0.23   0.820       -.0041    .0051778
   education |  -.0702355   .0352954    -1.99   0.047    -.1395569   -.0009141
      income |   .1116469   .0376914     2.96   0.003     .0376198     .185674
professional |   .1392548   .0881846     1.58   0.115    -.0339429    .3124524
     laborer |  -.0119926   .0826566    -0.15   0.885     -.174333    .1503477
  unemployed |   .0338301   .0806559     0.42   0.675    -.1245809    .1922411
       moved |   .1349295    .062373     2.16   0.031     .0124267    .2574323
       _cons |   2.118105   .1283842    16.50   0.000     1.865954    2.370256
------------------------------------------------------------------------------

. 
. reg decision revcategory mosul isisvictim female age education income professional laborer unemployed move
> d, cluster(id)

Linear regression                               Number of obs     =      5,338
                                                F(11, 594)        =     994.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5872
                                                Root MSE          =     1.0433

                                   (Std. Err. adjusted for 595 clusters in id)
------------------------------------------------------------------------------
             |               Robust
    decision |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 revcategory |   .3824339   .0068954    55.46   0.000     .3688916    .3959762
       mosul |   1.464816   .0639729    22.90   0.000     1.339176    1.590457
  isisvictim |   .0300727   .0307211     0.98   0.328    -.0302625    .0904079
      female |  -.0165518   .0575465    -0.29   0.774    -.1295711    .0964676
         age |  -.0003325   .0022172    -0.15   0.881    -.0046871    .0040221
   education |  -.0615869   .0329793    -1.87   0.062    -.1263572    .0031835
      income |    .099307   .0345329     2.88   0.004     .0314856    .1671284
professional |   .1440276   .0816893     1.76   0.078    -.0164074    .3044626
     laborer |   .0109573   .0771688     0.14   0.887    -.1405996    .1625143
  unemployed |   .0429581   .0739714     0.58   0.562    -.1023193    .1882354
       moved |   .1359222   .0585899     2.32   0.021     .0208537    .2509908
       _cons |   .1291014   .1203427     1.07   0.284    -.1072476    .3654505
------------------------------------------------------------------------------

. 
. reg punmalefemale i.malefemaletxt mosul judicialdenial agencyaffirm isisvictim female age education income
>  professional laborer unemployed moved, cluster(id)

Linear regression                               Number of obs     =      2,215
                                                F(18, 555)        =      46.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3172
                                                Root MSE          =     1.1249

                                                          (Std. Err. adjusted for 556 clusters in id)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                      punmalefemale |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                      malefemaletxt |
    male loyal worker (nasimloyal)  |  -.0132016   .0820938    -0.16   0.872    -.1744541     .148051
male opportunist worker (nasimopp)  |  -.0282723   .0685873    -0.41   0.680    -.1629948    .1064502
           fighter wife (sarawife)  |   .0500896   .0793914     0.63   0.528    -.1058548    .2060339
        female worker (saraworker)  |   .4330056   .0664536     6.52   0.000     .3024742    .5635369
      low-level wife (linalowwife)  |  -.0143295   .0825883    -0.17   0.862    -.1765532    .1478943
    high-level wife (linahighwife)  |    .287363   .0712516     4.03   0.000     .1474073    .4273187
                                    |
                              mosul |   .9613799   .1183854     8.12   0.000     .7288416    1.193918
                     judicialdenial |   .4545545   .1356809     3.35   0.001     .1880436    .7210653
                       agencyaffirm |    .870474    .210367     4.14   0.000     .4572611    1.283687
                         isisvictim |   .0763332   .0531767     1.44   0.152     -.028119    .1807853
                             female |  -.2069385   .0851545    -2.43   0.015    -.3742029    -.039674
                                age |  -.0010799   .0039367    -0.27   0.784    -.0088125    .0066526
                          education |  -.0774914   .0528417    -1.47   0.143    -.1812857    .0263029
                             income |   .1245111   .0532485     2.34   0.020     .0199179    .2291042
                       professional |   .1564153     .13084     1.20   0.232    -.1005869    .4134174
                            laborer |  -.1522416   .1217907    -1.25   0.212    -.3914686    .0869854
                         unemployed |   .0229287   .1217959     0.19   0.851    -.2163087     .262166
                              moved |   .1214389    .089311     1.36   0.174    -.0539901    .2968678
                              _cons |   1.157166   .1938471     5.97   0.000     .7764019    1.537929
-----------------------------------------------------------------------------------------------------

. 
. *Table 5. Punishment of ISIS (Ordered Probit Regression)
. *Long format
. 
. oprobit decision revcategory mosul isisvictim female age education income professional laborer unemployed 
> moved, cluster(id)

Iteration 0:   log pseudolikelihood = -8105.5864  
Iteration 1:   log pseudolikelihood = -5849.5034  
Iteration 2:   log pseudolikelihood = -5813.9318  
Iteration 3:   log pseudolikelihood = -5813.8562  
Iteration 4:   log pseudolikelihood = -5813.8562  

Ordered probit regression                       Number of obs     =      5,338
                                                Wald chi2(11)     =    1777.55
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -5813.8562               Pseudo R2         =     0.2827

                                   (Std. Err. adjusted for 595 clusters in id)
------------------------------------------------------------------------------
             |               Robust
    decision |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 revcategory |   .4159987   .0110066    37.80   0.000     .3944263    .4375712
       mosul |   1.599444     .07598    21.05   0.000     1.450526    1.748362
  isisvictim |   .0316916   .0344919     0.92   0.358    -.0359114    .0992945
      female |  -.0321453   .0638222    -0.50   0.614    -.1572345    .0929439
         age |  -.0009381   .0024834    -0.38   0.706    -.0058054    .0039292
   education |  -.0903265   .0375298    -2.41   0.016    -.1638835   -.0167694
      income |   .1012023   .0380621     2.66   0.008     .0266019    .1758027
professional |   .1903473   .0922818     2.06   0.039     .0094784    .3712163
     laborer |   .0440369   .0874699     0.50   0.615    -.1274009    .2154747
  unemployed |   .0715808    .081253     0.88   0.378    -.0876721    .2308337
       moved |   .1671643   .0651084     2.57   0.010     .0395542    .2947744
-------------+----------------------------------------------------------------
       /cut1 |   1.984107   .1472362                      1.695529    2.272684
       /cut2 |   2.866257   .1593443                      2.553948    3.178566
       /cut3 |    3.38879   .1647741                      3.065839    3.711741
       /cut4 |    3.91798   .1711906                      3.582453    4.253507
------------------------------------------------------------------------------

. 
. oprobit punmalefemale i.malefemaletxt mosul judicialdenial agencyaffirm isisvictim female age education in
> come professional laborer unemployed moved, cluster(id)

Iteration 0:   log pseudolikelihood = -3503.9779  
Iteration 1:   log pseudolikelihood = -3024.4348  
Iteration 2:   log pseudolikelihood = -3022.4504  
Iteration 3:   log pseudolikelihood = -3022.4497  
Iteration 4:   log pseudolikelihood = -3022.4497  

Ordered probit regression                       Number of obs     =      2,215
                                                Wald chi2(18)     =     558.06
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3022.4497               Pseudo R2         =     0.1374

                                                          (Std. Err. adjusted for 556 clusters in id)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                      punmalefemale |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                      malefemaletxt |
    male loyal worker (nasimloyal)  |   .0514561   .0768303     0.67   0.503    -.0991285    .2020408
male opportunist worker (nasimopp)  |   .0623926   .0704782     0.89   0.376    -.0757421    .2005273
           fighter wife (sarawife)  |   .0918224   .0741745     1.24   0.216    -.0535569    .2372017
        female worker (saraworker)  |   .4757321   .0671968     7.08   0.000     .3440289    .6074354
      low-level wife (linalowwife)  |   .0146518   .0771494     0.19   0.849    -.1365582    .1658618
    high-level wife (linahighwife)  |   .2876031   .0723471     3.98   0.000     .1458054    .4294009
                                    |
                              mosul |   1.019425   .1165776     8.74   0.000     .7909375    1.247913
                     judicialdenial |   .4351482   .1303066     3.34   0.001     .1797519    .6905445
                       agencyaffirm |   .9495856   .2029072     4.68   0.000     .5518948    1.347277
                         isisvictim |   .0916341    .048668     1.88   0.060    -.0037535    .1870217
                             female |  -.1804579    .079795    -2.26   0.024    -.3368533   -.0240625
                                age |  -.0017191   .0036657    -0.47   0.639    -.0089038    .0054655
                          education |  -.0443562   .0508122    -0.87   0.383    -.1439464    .0552339
                             income |   .1153693   .0482024     2.39   0.017     .0208943    .2098443
                       professional |   .1528959    .119447     1.28   0.201    -.0812159    .3870076
                            laborer |  -.1026022   .1171739    -0.88   0.381    -.3322588    .1270545
                         unemployed |   .0177886   .1147727     0.15   0.877    -.2071616    .2427389
                              moved |   .1187032   .0805929     1.47   0.141     -.039256    .2766625
------------------------------------+----------------------------------------------------------------
                              /cut1 |    .795493   .1899273                      .4232422    1.167744
                              /cut2 |   1.805174    .195433                      1.422133    2.188216
                              /cut3 |   2.477313   .1993314                      2.086631    2.867996
                              /cut4 |   3.057435   .2049652                      2.655711    3.459159
-----------------------------------------------------------------------------------------------------

. 
. *Table 6. Punishment of ISIS by Treatment Groups (OLS regression)
. *see wide format
. 
. *Table  7. Summary of Additional Variables used in SI Tables 4-6
. *Long format
. 
. sum alphapunishisis decision category punmalefemale

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
alphapunis~s |        596    3.359959    1.041488      1.125          5
    decision |      5,347     3.13746    1.622303          1          5
    category |      5,347    5.004301    2.584194          1          9
punmalefem~e |      2,370    2.654008     1.35605          1          5

. 
. *see also wide format
. 
. *Manuscript Table 2 Robustness Checks
. *Long format
. 
. reg punmalefemale femaleworkertxt, cluster(id)

Linear regression                               Number of obs     =        689
                                                F(1, 344)         =      40.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0245
                                                Root MSE          =     1.3246

                                      (Std. Err. adjusted for 345 clusters in id)
---------------------------------------------------------------------------------
                |               Robust
  punmalefemale |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
femaleworkertxt |   .4192703   .0655612     6.40   0.000     .2903191    .5482215
          _cons |   2.351744   .0653024    36.01   0.000     2.223302    2.480187
---------------------------------------------------------------------------------

. reg punmalefemale femaleworkertxt##c.agencyaffirm isisvictim female age education income professional labo
> rer unemployed moved, cluster(id)

Linear regression                               Number of obs     =        639
                                                F(12, 319)        =      22.01
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2987
                                                Root MSE          =     1.1378

                                                     (Std. Err. adjusted for 320 clusters in id)
------------------------------------------------------------------------------------------------
                               |               Robust
                 punmalefemale |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
               femaleworkertxt |
   female worker (saraworker)  |  -.0281543   .1459791    -0.19   0.847    -.3153578    .2590492
                  agencyaffirm |   .7991279   .2999149     2.66   0.008     .2090668    1.389189
                               |
femaleworkertxt#c.agencyaffirm |
   female worker (saraworker)  |   .8741721   .2616603     3.34   0.001     .3593741     1.38897
                               |
                    isisvictim |  -.0480235   .0970566    -0.49   0.621    -.2389753    .1429284
                        female |  -.3666869   .1414422    -2.59   0.010    -.6449643   -.0884096
                           age |    .001087   .0060401     0.18   0.857    -.0107965    .0129705
                     education |   .1234448   .0784967     1.57   0.117    -.0309919    .2778814
                        income |   .3608776    .074086     4.87   0.000     .2151186    .5066366
                  professional |  -.1783314   .2128222    -0.84   0.403    -.5970439    .2403811
                       laborer |  -.3710901   .1930226    -1.92   0.055    -.7508483    .0086681
                    unemployed |  -.2001388   .1987351    -1.01   0.315    -.5911359    .1908584
                         moved |   .2099435   .1328128     1.58   0.115    -.0513561     .471243
                         _cons |   1.132234   .3138445     3.61   0.000     .5147669      1.7497
------------------------------------------------------------------------------------------------

. reg punmalefemale femaleworkertxt##c.judicialdenial isisvictim female age education income professional la
> borer unemployed moved, cluster(id)

Linear regression                               Number of obs     =        687
                                                F(12, 343)        =      24.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2895
                                                Root MSE          =     1.1411

                                                       (Std. Err. adjusted for 344 clusters in id)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                   punmalefemale |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                 femaleworkertxt |
     female worker (saraworker)  |   .1582108   .1140729     1.39   0.166    -.0661597    .3825812
                  judicialdenial |   .2863566   .2408169     1.19   0.235    -.1873071    .7600203
                                 |
femaleworkertxt#c.judicialdenial |
     female worker (saraworker)  |   .5528525   .2233026     2.48   0.014     .1136376    .9920675
                                 |
                      isisvictim |   .0543874   .0925584     0.59   0.557    -.1276661     .236441
                          female |  -.4195792   .1387472    -3.02   0.003    -.6924816   -.1466767
                             age |   .0009023   .0054563     0.17   0.869    -.0098297    .0116343
                       education |   .1239456   .0741735     1.67   0.096    -.0219465    .2698378
                          income |   .5205188   .0739362     7.04   0.000     .3750933    .6659443
                    professional |  -.2096529   .2041562    -1.03   0.305    -.6112085    .1919028
                         laborer |  -.4208675   .1783932    -2.36   0.019    -.7717497   -.0699852
                      unemployed |  -.1522998   .1956351    -0.78   0.437    -.5370953    .2324957
                           moved |    .262829   .1280746     2.05   0.041     .0109185    .5147395
                           _cons |   1.130565   .3303265     3.42   0.001     .4808447    1.780286
--------------------------------------------------------------------------------------------------

. reg punmalefemale mosul##femaleworkertxt isisvictim female age education income professional laborer unemp
> loyed moved, cluster(id)

Linear regression                               Number of obs     =        689
                                                F(12, 344)        =      30.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3660
                                                Root MSE          =     1.0765

                                                    (Std. Err. adjusted for 345 clusters in id)
-----------------------------------------------------------------------------------------------
                              |               Robust
                punmalefemale |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                      1.mosul |   .7542197   .1458727     5.17   0.000     .4673051    1.041134
                              |
              femaleworkertxt |
  female worker (saraworker)  |   .0555556   .0663949     0.84   0.403    -.0750356    .1861467
                              |
        mosul#femaleworkertxt |
1#female worker (saraworker)  |   .6219634   .1193003     5.21   0.000     .3873135    .8566133
                              |
                   isisvictim |   .0354812   .0872354     0.41   0.684    -.1361006     .207063
                       female |  -.3008788   .1224182    -2.46   0.014    -.5416612   -.0600964
                          age |   -.002117   .0047911    -0.44   0.659    -.0115404    .0073065
                    education |   .0325106   .0743246     0.44   0.662    -.1136773    .1786984
                       income |   .2302289   .0697777     3.30   0.001     .0929843    .3674735
                 professional |  -.0997607   .1905676    -0.52   0.601     -.474585    .2750636
                      laborer |  -.2861343   .1710788    -1.67   0.095    -.6226265     .050358
                   unemployed |  -.1171061   .1692874    -0.69   0.490    -.4500749    .2158626
                        moved |   .1688123   .1225948     1.38   0.169    -.0723175     .409942
                        _cons |    1.63819   .2924374     5.60   0.000        1.063    2.213381
-----------------------------------------------------------------------------------------------

. 
. *Manuscript Table 2 Power Calculations
. *see wide format
. 
. log close
      name:  <unnamed>
       log:  C:\Users\swhitt\Desktop\Gender and Accountability Replication Files\SCT gender and accountabili
> ty replication data long format log file.log
  log type:  text
 closed on:  30 Nov 2023, 15:59:11
------------------------------------------------------------------------------------------------------------
